Feature Extraction Algorithms

Concepts of a feature and feature extraction algorithm are explained. Feature extraction is distinguished from feature selection and dimensionality reduction. Feature extraction is crucial in image processing, natural language processing, and speech recognition, as it simplifies data, improves machine learning model performance, and extracts meaningful information. The scope and purpose of principal component analysis are outlined. Coordinate system conversion and the resulting creation of a new set of variables are discussed. Principal components and their relative variance values are defined. Among linear algebra and matrix methods, concepts such as covariance, sample covariance matrix, population covariance matrix, eigenvector, and eigenvalue are introduced. The roles of eigenvalues and eigenvectors in principal component analysis are emphasized. Linear discriminant analysis achieves dimensionality reduction by projecting the original high-dimensional data onto a lower-dimensional space defined by linear combinations of the original features. Fisher's discriminant ratio is a measure used in Fisher's linear discriminant analysis. The mathematical intuition behind linear discriminant analysis is clarified. The steps involved in executing this analysis are presented. A comparison of principal component analysis is made with linear discriminant analysis.